Network Analysis Resources & Updates – Telegram
Network Analysis Resources & Updates
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🎞 Machine Learning with Graphs

💥Free recorded course by Jure Leskovec, Computer Science, PhD

💥Graphs are a general language for describing and analyzing entities with relations/interactions. There are many types of networks and graphs, such as social networks, communication and transaction networks, biomedine networks, brain networks, etc. In this course, we will take advantage of relational structure for better prediction.


📽 Watch

📜 Slides

📲Channel: @ComplexNetworkAnalysis

#video #course #Graph #Machine_Learning
👍2
📄Consensus clustering in complex networks

📘Journal: Scientific Reports(I.F=5.516)

🗓Publish year: 2012

📎Study paper

📲Channel: @ComplexNetworkAnalysis
#paper #Consensus_clustering
👍1
📄Network analysis approach to Likert-style surveys

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Journal: PHYSICAL REVIEW PHYSICS EDUCATION RESEARCH (I.F=2.359)

🗓Publish year: 2022

📎Study paper

📱Channel: @ComplexNetworkAnalysis
#paper #Likert_style #survey
📄Motif discovery algorithms in static and temporal networks: A survey

📘Journal: Journal of Complex Networks(I.F=2.011)

🗓Publish year: 2020

📎Study paper

📲Channel: @ComplexNetworkAnalysis
#paper #Motif #survey
👍2
🎞 Closeness Centrality & Betweenness Centrality: A Social Network Lab in R for Beginners

💥Free recorded course

💥So what then is “closeness” or “betweenness” in a network? How do we figure these things out and how do we interpret them? This video is part of a series where we give you the basic concepts and options, and we walk you through a Lab where you can experiment with designing a network on your own in R. Hosted by Jonathan Morgan and the Duke University Network Analysis Center.


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📲Channel: @ComplexNetworkAnalysis

#video #course #Closeness_Centrality #Betweenness_Centrality #code #R
📄Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale

📘Journal: PLOS ONE(I.F=3.752)

🗓Publish year: 2016

📎Study paper

📲Channel: @ComplexNetworkAnalysis
#paper #Clustering
📄A survey of game theory as applied to social networks

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Journal: T singhua Science and Technology (I.F=3.515)

🗓Publish year: 2020

📎Study paper

📱Channel: @ComplexNetworkAnalysis
#paper #game_theory #survey
👍1
🎞 Network Analysis in Systems Biology

💥Free recorded course by Avi Ma’ayan, PhD

💥An introduction to data integration and statistical methods used in contemporary Systems Biology, Bioinformatics and Systems Pharmacology research. The course covers methods to process raw data from genome-wide mRNA expression studies (microarrays and RNA-seq) including data normalization, differential expression, clustering, enrichment analysis and network construction. The course contains practical tutorials for using tools and setting up pipelines, but it also covers the mathematics behind the methods applied within the tools. The course is mostly appropriate for beginning graduate students and advanced undergraduates majoring in fields such as biology, math, physics, chemistry, computer science, biomedical and electrical engineering. The course should be useful for researchers who encounter large datasets in their own research. The course presents software tools developed by the Ma’ayan Laboratory

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📲Channel: @ComplexNetworkAnalysis

#video #course #Biology
🎞 Consul and Complex Networks

💥Free recorded course by James Phillips, Consul Lead at HashiCorp

💥A systematic overview of Consul's different network models, how they work, what kind of use cases they serve, and how prepared queries can help provide glue to keep service discovery simple across all.

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📲Channel: @ComplexNetworkAnalysis

#video #course #Consul
👍2
📄Network-based machine learning and graph theory algorithms for precision oncology

📘Journal: npj Precision Oncology(I.F=10.092)

🗓Publish year: 2017

📎Study paper

📲Channel: @ComplexNetworkAnalysis
#paper #machine_Learning #graph
📄Complex network approaches to nonlinear time series analysis

📘Journal: Physics Reports (I.F=25.6)

🗓Publish year: 2019

📎Study paper

📲Channel: @ComplexNetworkAnalysis
#paper #time_series
🎞 Machine Learning with Graphs: Applications of Graph ML

💥Free recorded course by Jure Leskovec, Computer Science, PhD

💥Graph machine learning can be applied in many scenarios, including the tasks of node classification, link prediction, graph classification, etc. Machine Learning at different levels of graphs usually demonstrate powerful capability in many specific tasks in different fields, ranging from protein folding, drug discovery, to recommender system, traffic prediction, among various other tasks.


📽 Watch

📜 Slides

💻Codes: part1 part2

📲Channel: @ComplexNetworkAnalysis

#video #course #Graph #Machine_Learning #code #python
👍2
📄New perspectives on analysing data from biological collections based on social network analytics

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Journal: Scientific Reports (I.F=4.996)

🗓Publish year: 2020

📎Study paper

📱Channel: @ComplexNetworkAnalysis
#paper #biological
📄Applications of network analysis to routinely collected health care data: a systematic review

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Journal: Journal of the American Medical Informatics Association (I.F=7.942)

🗓Publish year: 2018

📎Study paper

📱Channel: @ComplexNetworkAnalysis
#paper #Applications #health_care #review
🎓Analysis of the Structural Properties and Scalability of Complex Networks

📘A DISSERTATION SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA

🗓Publish year: 2018

📎Study dissertation

📱Channel: @ComplexNetworkAnalysis
#dissertation #scalability